Yong Fan, PhD

faculty photo
Assistant Professor of Radiology
Department: Radiology

Contact information
Department of Radiology
Perelman School of Medicine
University of Pennsylvania
Richards Building, 7th floor
3700 Hamilton Walk
Philadelphia, PA 19104-6116
Office: 215-746-4065
Education:
PhD (Pattern Recognition and Intelligent Systems)
Chinese Academy of Sciences, Beijing, China, 2003.
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Description of Research Expertise

Dr. Fan’s interests are in the field of imaging analytics, machine learning, and pattern recognition, and more generally in computational imaging.

Much of his work has been focusing on development and application of advanced machine learning techniques that quantify morphology and function from medical images, integrate multimodal information to aid diagnosis and prediction of clinical outcomes, and guide personalized treatments. The methodological focus has been on the general field of artificial intelligence (AI), with emphasis on machine learning methods applied to complex and large imaging and clinical data. The image analytic methods are being and to be developed include functional connectomics, radiomics and radiogenomics, image registration and segmentation, and personalized neuromodulatory therapies. On the clinical side, his primary focus is on applications in clinical neuroscience, in cancer, and in chronic kidney disease, aiming to develop precision diagnostic tools using machine learning and pattern analysis. The clinical research studies include brain development, brain diseases such as Alzheimer's, schizophrenia, depression, and addiction, pediatric kidney diseases, and predictive modeling of treatment outcomes of cancer patients such as rectal and lung cancers.

Selected Publications

Hongming Li, Maya Galperin-Aizenberg, Daniel Pryma, Charles B. Simone II, and Yong Fan : Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy Radiotherapy & Oncology Page: 1-9, July 2018 Notes: in press, 4 July 2018.

Hongming Li, Xiaofeng Zhu, Yong Fan: Identification of multi-scale hierarchical brain functional networks using deep matrix factorization. Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) LNCS 11072: 223–231, September 2018.

Hongming Li and Yong Fan: Identification of temporal transition of functional states using recurrent neural networks from functional MRI. Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) LNCS 11072: 232-239, September 2018.

Hongming Li and Yong Fan: Brain decoding from functional MRI using long short-term memory recurrent neural networks. Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) LNCS 11072: 320–328, September 2018.

Hongming Li and Yong Fan: Non-rigid image registration using self-supervised fully convolutional networks without training data. Proceedings of IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) Page: 1075-1078, April 2018 Notes: 10.1109/ISBI.2018.8363757.

Reagan R. Wetherill, Hengyi Rao, Nathan Hager, Jieqiong Wang, Teresa R. Franklin, Yong Fan: Classifying and Characterizing Nicotine Use Disorder with High Accuracy Using Machine Learning and Resting-State fMRI. Addiction Biology Page: 1-11, June 2018 Notes: in press. DOI:10.1111/adb.12644.

Hongming Li, Theodore D. Satterthwaite, and Yong Fan: Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks Proceedings of IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) Page: 101-104, April 2018.

Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, and Yong Fan: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation Medical Image Analysis 43: 98-111, January 2018.

Xiaofeng Zhu, Weihong Zhang, Yong Fan: A robust reduced rank graph regression method for neuroimaging genetics analysis. Neuroinformatics 16(3-4): 351-361, October 2018.

Qiang Zheng, Susan L. Furth, Gregory E. Tasian, Yong Fan: Computer aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. Journal of Pediatric Urology October 2018 Notes: DOI: 10.1016/j.jpurol.2018.10.020

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Last updated: 12/03/2018
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